In summary, we trained five low-level RL agents to invest in a set of assets according to prototypical personality traits [1]}. We now combine their actions using a high-level RL agent that uses a customer's spending behavior as manifested in their transaction history. This spending behavior is evinced in behavioral trajectories that we extracted from the state space of a RNN that predicts personality from financial transactions [2]}. We illustrate this in a flow diagram in Figure REF .
